Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (7): 2051-2055.DOI: 10.11772/j.issn.1001-9081.2015.07.2051

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Face recognition algorithm based on cluster-sparse of active appearance model

FEI Bowen, LIU Wanjun, SHAO Liangshan, LIU Daqian, SUN Hu   

  1. Software Engineering Institute, Liaoning Technical University, Huludao Liaoning 125105, China
  • Received:2014-12-31 Revised:2015-03-25 Online:2015-07-10 Published:2015-07-17

基于主动表观模型的稀疏聚类人脸识别算法

费博雯, 刘万军, 邵良杉, 刘大千, 孙虎   

  1. 辽宁工程技术大学 软件学院, 辽宁 葫芦岛 125105
  • 通讯作者: 刘万军(1959-),男,辽宁北镇人,教授,博士生导师,CCF高级会员,主要研究方向:数字图像处理、运动目标检测与跟踪,liuwanjun@lntu.edu.cn
  • 作者简介:费博雯(1991-),女,辽宁抚顺人,硕士研究生,主要研究方向:图像处理、模式识别; 邵良杉(1961-),男,辽宁凌源人,教授,博士生导师,主要研究方向:数据挖掘; 刘大千(1992-),男,辽宁铁岭人,硕士研究生,主要研究方向:目标跟踪。
  • 基金资助:

    国家自然科学基金资助项目(61172144);辽宁省科技攻关计划项目(2012216026)。

Abstract:

The recognition accuracy rate of traditional Sparse Representation Classification (SRC) algorithm is relatively low under the interference of complex non-face ingredient, large training sample set and high similarity between the training samples. To solve these problems, a novel face recognition algorithm based on Cluster-Sparse of Active Appearance Model (CS-AAM) was proposed. Firstly, Active Appearance Model (AAM) rapidly and accurately locate facial feature points and to get the main information of the face. Secondly, K-means clustering was run on the training sample set, the images with high similarity degree were assigned to a category and the clustering center was calculated. Then, the center was used as atomic to structure over-complete dictionary and do sparse decomposition. Finally, face images were classified and recognized by computing sparse coefficients and reconstruction residuals. The face images with different samples and different dimensions from ORL face database and Extended Yale B face database were tested for comparing CS-AAM with Nearest Neighbor (NN), Support Vector Machine (SVM), Sparse Representation Classification (SRC), and Collaborative Representation Classification (CRC). The recognition rate of CS-AAM algorithm is higher than other algorithms with the same samples or the same dimensions. Under the same dimensions, the recognition rate of CS-AAM is 95.2% when the selected number of samples is 210 on ORL face database; the recognition rate of CS-AAM is 96.8% when the selected number of samples is 600 on Extended Yale B face database. The experimental results demonstrate that the proposed method has higher recognition accuracy rate.

Key words: face recognition, Sparse Representation Classification (SRC), Active Appearance Model (AAM), sparse clustering, over-complete dictionary

摘要:

在复杂的非人脸成分干扰以及训练样本过大、训练样本之间相似度较高的条件下,原始稀疏表示分类(SRC)算法识别准确率较低。针对上述问题,提出一种基于主动表观模型的稀疏聚类(CS-AAM)人脸识别算法。首先,利用主动表观模型快速、准确地对人脸特征点进行定位,获取主要人脸信息;然后,对训练样本进行K-means聚类,将相似程度高的图像分为一类,计算聚类中心,将该中心作为原子构造过完备字典并进行稀疏分解;最后,计算稀疏系数和重构残差对人脸图像进行分类、识别。将该算法与最近邻(NN)、支持向量机(SVM)、稀疏表示分类(SRC)、协同表示分类(CRC)人脸识别算法在ORL和Extended Yale B人脸数据库上对不同样本数及不同维数的人脸图像分别进行识别率测试,在相同样本数或相同维数情况下CS-AAM算法识别率均高于其他算法。在ORL人脸库中选取样本数为210时,相同维数条件下CS-AAM算法识别率为95.2%;在Extended Yale B人脸库上选取样本数为600时,CS-AAM算法识别率为96.8%。实验结果表明,该算法能够有效地提高人脸图像的识别准确率。

关键词: 人脸识别, 稀疏表示分类, 主动表观模型, 稀疏聚类, 过完备字典

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